[Ascend]zzf/add equal op (#739)

* add equal op for ascend

* add unittest for equal op

* add unittest for equal op

* add unittest for equal op

* add unittest for equal op

* add torch.equal to dipu

* Update diopi_functions.yaml

dipu/SupportedDiopiFunctions.txt

@@ -85,6 +85,7 @@ diopiEq
 diopiEqInp
 diopiEqInpScalar
 diopiEqScalar
+diopiEqual
 diopiErfinv
 diopiErfinvInp
 diopiExp

dipu/scripts/autogen_diopi_wrapper/autogen_diopi_wrapper.py

@@ -324,18 +324,21 @@ def get_function_return_param_from_schema(schema):
         args = return_params[i]
         inplace_match = re.search("Tensor\([a-zA-Z]+!\)", args)
         pure_out_match = re.search("Tensor[ ,]?", args)
+        bool_out_match = re.search("bool", args)
         if inplace_match is not None:
             arg_label = re.sub(".*(\(.*\))", r"\1", inplace_match.group())
             index = schema.find(arg_label) + len(arg_label)
             param = re.search("[a-zA-Z0-9_::]+", schema[index:]).group()
             params.append(param)
-        elif inplace_match is None and pure_out_match is not None:
+        elif pure_out_match is not None:
             name_from_schema = re.sub("\(?Tensor[ ]+([\w\d_]+)\)?", R"\1", args)
             if name_from_schema == args:
                 name = "out" + (str(i) if len(return_params) > 1 else "")
             else:
                 name = name_from_schema
             params.append(name)
+        elif bool_out_match is not None:
+            params.append("out")
     return params
 
 

dipu/scripts/autogen_diopi_wrapper/diopi_functions.yaml

@@ -358,6 +358,11 @@
     }
   interface: diopiEqInp(ctx, self, other)
 
+- schema: equal(Tensor self, Tensor other) -> bool
+  custom_code_at_the_beginning: |
+    bool out;
+  interface: diopiEqual(ctx, &out, self, other)
+
 - schema: "lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)"
   interface: diopiLtScalar(ctx, out, self, other)
 

dipu/tests/python/unittests/test_equal.py

@@ -0,0 +1,36 @@
+# Copyright (c) 2023, DeepLink.
+import torch
+import math
+import torch_dipu
+from torch_dipu.testing._internal.common_utils import TestCase, run_tests, onlyOn
+
+
+class TestEqual(TestCase):
+    @onlyOn("NPU")
+    def test_equal_1(self):
+        device = torch.device("dipu")
+        x = torch.rand(3, 4).to(device)
+        self.assertEqual(True, torch.equal(x, x))
+
+    @onlyOn("NPU")
+    def test_equal_2(self):
+        device = torch.device("dipu")
+        x = torch.rand(3, 4).to(device)
+        self.assertEqual(False, torch.equal(x, x.to(torch.float16)))
+
+    @onlyOn("NPU")
+    def test_equal_3(self):
+        device = torch.device("dipu")
+        x = torch.zeros(3, 4).to(device)
+        self.assertEqual(True, torch.equal(x, x.to(torch.float16)))
+
+    @onlyOn("NPU")
+    def test_equal_4(self):
+        device = torch.device("dipu")
+        x = torch.rand(3, 4).to(device)
+        y = torch.rand(3, 5).to(device)
+        self.assertEqual(False, torch.equal(x, y))
+
+
+if __name__ == "__main__":
+    run_tests()